Advances in computing technologies have led to a proliferation of digital images in modern society. Digital images may include graphics included in graphical user interfaces, photographs, and any other digitally represented images. Such digital images are typically stored as image data (e.g., as one or more image files) in computer memory systems.
For computing systems that store significant amounts of data and/or have limited memory resources, the amount of memory resources occupied by image data may be an important concern. To help minimize the amount of memory resources needed for storing image data, tools designed to process and reduce the amount of image data used to represent digital images have been developed. Such tools are generally capable of reducing the size of image data and, consequently, the memory footprint of the image data. For example, a conventional tool typically processes an image data file using a brute force, multi-iteration routine that may produce a resulting image data file that is smaller in size than the beginning image data file. However, such a routine is typically a time consuming process, and a tool implementing such a routine often fails to produce an image data file that cannot be further optimized (e.g., the image data file is not as small as possible). In addition, for a conventional tool that uses a brute force, multi-iteration routine to optimize image data, an operator of the tool commonly experiences difficulties and/or inefficiencies in attempting to balance the number of iterations with diminishing returns in optimization results.